Reducing database scans for on-shelf utility mining

Guo Cheng Lan, Tzung Pei Hong, Vincent Shin-Mu Tseng

Research output: Contribution to journalArticle

3 Scopus citations

Abstract

Utility mining has recently been an emerging topic in the field of data mining. It finds out high-utility itemsets by considering both the profits and quantities of items in transactions. In real applications, however, utility mining may have a bias if items are not always on shelf. On-shelf utility mining is then proposed, which considers not only individual profit and quantity of each item in a transaction but also common on-shelf time periods of a product combination. In the past, a two-phase on-shelf utility mining was proposed to discover the desired patterns in on-shelf utility mining. It, however, adopted the level-wise mining way to find the patterns. To speed up the execution efficiency, a three-scan mining approach is thus proposed in the paper to efficiently discover high on-shelf utility itemsets. The proposed approach utilizes an itemset-generation mechanism to prune redundant candidates early and to systematically check the itemsets from transactions. At last, the experimental results on synthetic datasets show the proposed approach has a better performance than the previous one.

Original languageEnglish
Pages (from-to)103-112
Number of pages10
JournalIETE Technical Review (Institution of Electronics and Telecommunication Engineers, India)
Volume28
Issue number2
DOIs
StatePublished - 1 Mar 2011

Keywords

  • Data mining
  • High-utility itemsets
  • On-shelf data
  • Utility mining

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